DE 103 37 932 A1 discloses a method for minimizing streak artifacts during modular k-space scanning in magnetic resonance imaging, wherein the method includes the following acts: defining an odd integer k-space scanning module number Nφ=2n+1 that defines the number of incrementally rotated repeated modules of the k-space scanning process; selecting, by a slice selection gradient, any slice in the region of the object to be examined; and acquiring data for all Nφ angle-dependent k-space scanning modules in the selected slice such that each k-space scanning module has an azimuthal distance of Δφ/2=360°/(2·Nφ) from both adjacent k-space scanning modules, wherein the direction of scanning of adjacent k-space scanning modules alternates.
DE 10 2011 086 369 A1 discloses a method for creating MR angiography images and a corresponding magnetic resonance device. The method relates to the creation of MR angiography images of a predetermined three-dimensional volume segment of a living object undergoing examination, using a magnetic resonance device. For this, the following acts are performed: magnetic resonance data in the volume segment is acquired by radial acquisition of a k-space; the magnetic resonance data is analyzed in order to subdivide the magnetic resonance data into groups, with each group including only the magnetic resonance data that corresponds to a particular heartbeat phase of the heart of the object undergoing examination; the MR angiography images are created on the basis of only the magnetic resonance data of one of these groups.
Robert Grimm, Sebastian Furst, Michael Souvatzoglou, Christoph Forman, Jana Hutter, Isabel Dregely, Sibylle I. Ziegler, Berthold Kiefer, Joachim Hornegger, Kai Tobias Block, Stephan G. Nekolla: Self-gated MRI motion modeling for respiratory motion compensation in integrated PET/MRI, in Medical Image Analysis 19, pp. 110-120 (2015), describes how accurate localization and uptake quantification of lesions in the chest and abdomen using PET imaging is challenged by respiratory difficulties during the examination. The paper describes how a so-called “stack-of-stars” MRI acquisition on integrated PET/MRI systems may be used to derive a high-resolution motion model, how many respiratory phases need to be differentiated, how much MRI scan time is required, and how the model may be employed for motion-corrected PET reconstruction. So-called MRI “self-gating” is applied to perform respiratory gating of the MRI data and simultaneously acquired PET raw data. After gated PET reconstruction, the MRI motion model is used to fuse the individual gates into a single, motion-compensated volume with high signal-to-noise ratio (SNR). The proposed method is evaluated in vivo for 15 clinical patients. The gating requires 5-7 bins to capture the motion to an average accuracy of 2 mm. With 5 bins, the motion-modeling scan may be shortened to 3-4 min. The motion-compensated reconstructions show significantly higher accuracy in lesion quantification in terms of standardized uptake value (SUV) and different measures of lesion contrast compared to ungated PET reconstruction. Furthermore, unlike gated PET reconstructions, the motion-compensated reconstruction does not lead to SNR loss.
Self-gating is described for example in: Robert Grimm, Simon Bauer, Berthold Kiefer, Joachim Hornegger, and Tobias Block: Optimal Channel Selection for Respiratory Self-Gating Signals, in 3ISMRM 21st Annual Meeting & Exhibition, SMRT 22nd Annual Meeting, 20-26 Apr. 2013, Salt Lake City, Utah, USA.
The GRASP technique is described for example in: Kai Tobias Block, Li Feng, Robert Grimm, Hersh Chandarana, Ricardo Otazo, Christian Geppert, Daniel K. Sodickson: GRASP: Tackling the Challenges of Abdominopelvic DCE-MRI, in MAGNETOM Flash May 2014, pp. 16-22; in: Philipp Riffel, Kai Tobias Block: Free-breathing DCE-MRI of the Kidney using GRASP, in MAGNETOM Flash (66) March 2016, pp. 56-58; or in: Li Feng, Kai Tobias Block, Robert Grimm, Hersh Chandarana, Sungheon Kim, Jian Xu, Leon Axel, Daniel K. Sodickson, Ricardo Otazo: Golden-Angle Radial Sparse Parallel MRI: Combination of Compressed Sensing, Parallel Imaging, and Golden-Angle Radial Sampling for Fast and Flexible Dynamic Volumetric MRI, in Magn. Reson. Med. 2014 September, vol. 72(3), pp. 707-17.